AI-Augmented Data Security in Cloud Migration: Leveraging Generative AI and Snowflake for Secure Financial Data Processing

This paper offers a complete framework combining Snowflake's cloud data platform with AI- augmented methods to improve data security during cloud migration. Six strategic phases—from preprocessing to evaluation—each help to contribute to better performance measures in the suggested approach. Visual studies show a notable decrease in system response time (250 ms to 140 ms) as well as a continuous increase in security score (70% to 95%), and detection accuracy (68% to 94%). Moreover, accuracy and precision measures show clear development throughout the phases, reaching respectively 93% and 91%. These results confirm the effectiveness of using generative artificial intelligence and scalable data warehousing in protecting financial data from advanced cyber threats, therefore offering a scalable and responsive solution catered for safe digital transformation in the financial industry. Keyword Used- AI-Augmented Security, Cloud Migration, Snowflake Integration, Financial Data Protection, Generative AI, Anomaly Detection, Response Time Optimization, Detection Accuracy